Recently, the aerosol microphysics submodel MADE3 (Modal Aerosol Dynamics
model for Europe, adapted for global applications, third generation) was
introduced as a successor to MADE and MADE-in. It includes nine aerosol
species and nine lognormal modes to represent aerosol particles of three
different mixing states throughout the aerosol size spectrum. Here, we
describe the implementation of the most recent version of MADE3 into the
ECHAM/MESSy Atmospheric Chemistry (EMAC) general circulation model, including
a detailed evaluation of a 10-year aerosol simulation with MADE3 as part of
EMAC.

We compare simulation output to station network measurements of near-surface
aerosol component mass concentrations, to airborne measurements of aerosol
mass mixing ratio and number concentration vertical profiles, to ground-based
and airborne measurements of particle size distributions, and to station
network and satellite measurements of aerosol optical depth. Furthermore, we
describe and apply a new evaluation method, which allows a comparison of model
output to size-resolved electron microscopy measurements of particle
composition. Although there are indications that fine-mode particle deposition
may be underestimated by the model, we obtained satisfactory agreement with
the observations. Remaining deviations are of similar size to those identified
in other global aerosol model studies.

Thus, MADE3 can be considered ready for application within EMAC. Due to its
detailed representation of aerosol mixing state, it is especially useful for
simulating wet and dry removal of aerosol particles, aerosol-induced
formation of cloud droplets and ice crystals as well as aerosol–radiation
interactions. Besides studies on these fundamental processes, we also plan to
use MADE3 for a reassessment of the climate effects of anthropogenic aerosol
perturbations.

As an improvement to its predecessors, MADE3 includes computationally
efficient and consistent representations of three different aerosol mixing
states in each of three different size ranges, which can be advantageous for
many other applications. For instance,
we aim to use MADE3 for assessments of aerosol–ice cloud
interactions. Particles composed of compounds with no or very low
water solubility (in the following denoted as “insoluble particles”),
such as mineral dust or black carbon particles, can serve as ice nuclei
initiating ice formation in mixed-phase or cirrus clouds
(e.g., Lohmann and Feichter, 2005; Hoose and Möhler, 2012). The ice formation efficiency of these
particles strongly depends on their size, surface area, and state of
mixing with soluble aerosol species.
To simulate these effects, climate models should allow for explicit predictions
of the number concentration, size distribution, and mixing state of
aerosol particles
containing insoluble components. In the first generation of model
studies on the role of ice nuclei in the global climate system, bulk
aerosol schemes were applied
(Lohmann et al., 2004; Hendricks et al., 2005, 2011).
This implied that the number of potential ice nuclei had to be
estimated from aerosol mass assuming typical aerosol size
distributions. Advanced aerosol schemes allowing the explicit
simulation of the aerosol number concentration and size distribution
were applied in more recent studies (e.g., Lohmann and Hoose, 2009; Gettelman et al., 2012; Kuebbeler et al., 2014; Zhou and Penner, 2014).
However, the individual number concentrations of insoluble particles in different
size ranges and mixing states could only partly be quantified with these
approaches. MADE3 has the advantage that it allows explicit simulations of the
number concentration, size distribution (assuming lognormal modes with fixed widths), and mixing state (external or
internal mixture) of aerosol particles containing insoluble components. Hence,
the new aerosol scheme opens new opportunities for the simulation of aerosol
effects on ice clouds.

We intend to use the enhanced capabilities of MADE3 to update the results of our
previous studies on the health and climate impacts of the transport sectors
(Corbett et al., 2007; Lauer et al., 2009; Winebrake et al., 2009; Righi et al., 2011, 2013, 2015, 2016; Lund et al., 2012).
Such model applications will be the subject of future investigations. As a first
step towards these studies, the present article provides a detailed description
of the implementation of MADE3 into a global chemistry–climate model as well as
an evaluation of a first reference simulation.

The ability of the MADE3 algorithms to solve the gas–aerosol partitioning
(outside clouds), new particle formation, and coagulation parts of the aerosol
dynamics equation was demonstrated by Kaiser et al. (2014). For the solution of
the full equation, a number of further processes have to be considered, namely
particle and precursor emissions; particle transport by advection, convection,
and turbulent diffusion; aerosol precursor chemistry in the gas and liquid phases; and cloud
and precipitation scavenging of aerosols, as well as their dry deposition and
sedimentation. Hence, we describe here the implementation of MADE3 into the
atmospheric chemistry general circulation model EMAC
(ECHAM/MESSy Atmospheric Chemistry; Jöckel et al., 2010, 2016),
which includes further submodels to represent these processes
(Sect. 2). Subsequently, in Sect. 3, we present
an evaluation of the performance of EMAC with MADE3 as a global aerosol model.
The evaluation is accomplished by comparison of EMAC aerosol simulations to
observational data from a multitude of different sources, including station
networks, airborne measurements, laboratory analyses of in situ sampled
particles, and satellite data. The main conclusions of this study are summarized
in Sect. 4. Appendix A
provides a list of the acronyms used in this article. Details of the aerosol
scavenging scheme are explained in Appendix B.
Descriptions of the chemistry mechanisms considered are provided in the Supplement.

The work presented in this paper is partly based on the PhD thesis by
J. C. Kaiser (Kaiser, 2016). We therefore explain to the reader that
significant parts of the text in the abstract, Sects. 2 and 3, and
Appendix B already appeared in Kaiser (2016).

2.1 EMAC setup

The EMAC model is a numerical chemistry and
climate simulation system that includes submodels describing tropospheric and
middle atmospheric processes and their interaction with oceans, land, and human
influences (Jöckel et al., 2010). It uses the second version of MESSy to link
multi-institutional computer codes. The core
atmospheric model is the ECHAM5 (fifth-generation European Centre Hamburg) general
circulation model (Roeckner et al., 2006). For the present study, we
applied EMAC (ECHAM5 version 5.3.02, MESSy version 2.53) in the
T42L19 resolution, i.e., with a spherical truncation of T42 (corresponding to
a quadratic Gaussian grid of approximately 2.8 by 2.8 degrees in latitude and
longitude) with 19 vertical hybrid sigma-pressure levels up to 10 hPa. The
applied model setup comprised the submodels given in
Table 1. A model time step length Δt of 30 min
was used and a temporal resolution for the simulation output of 12 h.

We simulated 11 years in “nudged mode”; i.e., wind divergence and
vorticity, temperature, and logarithm of the surface pressure were relaxed
towards ERA-Interim reanalyses for the years 1995–2005.
The first simulated year is regarded as the (aerosol) spin-up phase, so that
our evaluation only takes into account the 10 years that followed.

Emissions of both gases and aerosol particles enter the EMAC atmosphere
through
the submodels OFFEMIS, for prescribed emissions, and ONEMIS, for so-called
online emissions that depend on the dynamics of the atmosphere (e.g., wind
speed) and the state of its lower boundary (e.g., sea surface temperature). The
emission setup used here is described in a separate subsection
(Sect. 2.4). Prescribed emissions are representative of the year
2000. The production of nitrogen oxides (NOx, i.e., NO and NO2) from
lightning was taken into account by the submodel LNOX, using a parameterization
by Price and Rind (1992), which is based on convective cloud top height as the
driving parameter. The parameterization was tuned to match global total
emissions within the observed range (Schumann and Huntrieser, 2007).

Aerosol particle transport is part of the tracer advection and vertical
diffusion schemes of the base model ECHAM5 and of the convective transport
submodel CVTRANS. Horizontal diffusion of particles is not considered in ECHAM5,
but it is anyway not expected to contribute significantly to transport on the
scales of the model grid boxes as used here.

We used the submodel MECCA to simulate atmospheric gas-phase chemistry. For
computational efficiency, the simplified tropospheric chemistry scheme that was
created by Lauer et al. (2007) was used. It includes 34 gases and 60 chemical
reactions (47 gas-phase and 13 photolysis reactions) to describe
NOx-HOx-CH4-CO-O3 chemistry and the tropospheric sulfur
cycle (see the Supplement for more details). The photolysis rates are calculated by the submodel JVAL.
Heterogeneous reactions, i.e., reactions of trace gases on or with aerosol particle surfaces,
are not included. Note, however, that reactions on cloud droplet surfaces are
included via the cloud-phase chemistry (see Sect. 2.3 and the Supplement).

MADE3, which is used for the representation of aerosol microphysics, will be
described in Sect. 2.2. To avoid convoluting the results with
feedbacks from the simulated aerosol on model dynamics, we switch off the
feedback of the MADE3 aerosol on clouds and radiation in the model
configuration described here. Before such feedbacks will be considered, the
quality of the MADE3 aerosol has to be proven, which is the purpose of this study.

Deposition of aerosol particles is handled in EMAC by the submodels DDEP,
which uses the so-called “big leaf” approach assuming that deposition fluxes
within the canopy have the same relative responses to the environment as any
single leaf, and that the scaling from leaf to canopy is therefore linear
(Sellers et al., 1996); SEDI, for sedimentation (gravitational settling); and
SCAV, for wet deposition. The latter required some MADE3-specific
modifications; see Sect. 2.3.

Optical properties of aerosol particles, which are considered to compute aerosol
optical depth (AOD) for comparison with satellite data (Sect. 3.5),
are determined by the submodel AEROPT. The lookup tables applied in AEROPT
are the same for MADE3 and its predecessor MADE. Hence, we used the MADE
tables that were created by Lauer et al. (2007) with the help of the software
libRadtran (Mayer and Kylling, 2005). Further details on these calculations are provided
by Dietmüller et al. (2016).

Cloud properties are calculated by the submodels CLOUD (stratiform clouds) and
CONVECT (convective clouds) in EMAC. For stratiform clouds, we selected the
standard ECHAM5 single-moment cloud scheme (Roeckner et al., 2003), i.e., a scheme
that only considers water and ice mass but no droplet or ice crystal
numbers. Although previous studies with the MADE3 predecessors were carried out
with two-moment cloud schemes, a single-moment scheme is sufficient here,
as we do not
attempt to quantify the climatic impact of aerosol particles. This will be the
subject of follow-up studies, however. Instead of the Tompkins (2002)
method to calculate fractional cloud cover (as described
by Roeckner et al., 2003), we choose the parameterization developed by
Sundqvist et al. (1989). The high numerical stability of this scheme is
advantageous for multi-year climate simulations. For convective clouds, we
choose the original ECHAM5 scheme
(Roeckner et al., 2003), which is based on work by Tiedtke (1989) and
Nordeng (1994), with modifications by Brinkop and Sausen (1997).

2.2 MADE3 v3.0

MADE3 was described in detail by Kaiser et al. (2014). Therefore, we only briefly
repeat its main characteristics here and in Fig. 1. The aerosol is represented by the modal
approach, namely with nine modes that represent different particle mixing states
and different particle size ranges.
Each of the Aitken, accumulation, and
coarse-mode size ranges in MADE3 includes three modes: one for
particles fully composed of water-soluble species, one for particles
mainly composed of insoluble material (i.e., insoluble particles
with only very thin coatings of soluble material), and one for
mixed particles (i.e., particles composed of soluble material including
insoluble immersions). In the following, we will refer to these modes
as “soluble”, “insoluble”, and “mixed” modes, respectively. The
considered components that make up these modes
are sulfate (SO4), ammonium (NH4), nitrate (NO3), sea spray (SS)
components other than chloride (mainly sodium; Na), chloride (Cl), particulate
organic matter (POM), black carbon (BC), mineral dust (DU), and aerosol water
(H2O). Different from the MADE3 box model version presented by
Kaiser et al. (2014), where the insoluble modes were dominated by BC
and mineral dust, we now also assign hydrophobic POM to the insoluble
modes during emission (see below) in order to describe interactions
of this aerosol component with clouds more consistently. Observations also show
that BC is mostly emitted internally mixed with POM (e.g., Petzold et al., 2013).

MADE3 calculates changes in the particle number concentration,
size distribution, and composition due to gas–particle partitioning,
particle coagulation, and new particle formation. For the gas–particle partitioning of semi-volatile species,
i.e., ammonia (NH3)∕NH4, nitric acid (HNO3)∕NO3, and
hydrochloric acid (HCl)∕Cl, an equilibrium approach is applied, where
condensation towards the coarse-mode particles is limited to the kinetically
possible fluxes. Sulfuric acid (H2SO4) and secondary organic aerosol
(SOA) precursors are assumed to condense irreversibly on the particles.
The amount of condensable H2SO4 is calculated online by the model
using the corresponding production rate as provided by the chemical scheme.
The amount of condensable SOA is prescribed in terms of an effective emission
of SOA from natural terpenes based on Dentener et al. (2006). The aerosol
dynamics equation is solved by applying a combination of analytical approximations
and process-specific numerical solvers. For the details of this approach,
we refer to Kaiser et al. (2014) and references therein. In addition to some technical
changes between the MADE3 version described by Kaiser et al. (2014) (v2.0b) and
the one used here (v3.0), we modified the treatment of new particles upon
nucleation events, as
well as the criterion for transferring particles from the insoluble to the
mixed modes, denoted as the aging criterion, as follows.

In the original version of the model, the transfer to the mixed modes was
induced as soon as insoluble particles obtained a liquid
coating of a critical size. We now neglect aerosol water in this aging
calculation and, correspondingly, in the target mode assignment upon particle
coagulation. Hence, only the water-soluble components of the coating are taken
into account. In this way, we interpret water uptake as a consequence
of particle aging rather than as the cause of it. We further neglect
the POM fraction in these model operations since its role in the aging
process is still uncertain. Particles from the
insoluble modes are now transferred to the mixed modes if the sum of the soluble
inorganic component masses exceeds 10 % of the modal dry mass. This
assumption is supported by laboratory and field measurements as
reported by Svenningsson et al. (1994), Khalizov et al. (2009), and Liu et al. (2013).
Correspondingly, we
assign particles that result from coagulation of insoluble modes with mixed or
soluble modes to an insoluble mode when the resulting soluble inorganic
contribution to dry mass is less than 10 %, and to a mixed mode
otherwise.

Concerning nucleation, we now account for initial growth of particles at
unresolved temporal and spatial scales by rescaling the formation rate of
H2SO4–H2O particles with a mode median wet diameter of
3.5 nm (as it
was formerly implemented) to a formation rate of SO4 particles with a mode
median dry diameter of 10 nm. This basically corresponds to
redistributing the nucleated mass into larger particles, assuming a lognormal
size distribution with the same width but with a larger median diameter, which
results in a decrease in nucleated particle number. With this modification, the
model seems to perform better at accurately simulating particle evolution, as
evidenced by the comparisons of number concentrations and size distributions
to observations in the free troposphere, where nucleation is the major source
of ultrafine particles
(see Sect. 3.2
and 3.3). We base this updated assumption on new particle
formation measurements as reported by, e.g., Modini et al. (2009),
Kerminen et al. (2010), Boulon et al. (2011), Matsui et al. (2011),
Young et al. (2013), García et al. (2014), Chandra et al. (2016),
Giamarelou et al. (2016), and Ueda et al. (2016).

When coupling the MADE3 aerosol to the cloud processing submodel SCAV
(see Appendix B), we assume
that the insoluble modes are hydrophobic, whereas we consider mixed and soluble
modes to be hydrophilic. Hence, only particles in the mixed and soluble modes undergo
liquid nucleation scavenging; i.e., they can serve as nuclei for cloud
droplet formation. In contrast,
ice nucleation scavenging is considered to be less efficient for purely soluble
particles (see Appendix B1). Analogous to
Aquila et al. (2011), we assume that 5 % of the soluble particles
are incorporated into ice crystals (ice nucleation scavenging ratio of
0.05) in cirrus clouds (T≤238.15K), consistent with
scavenging ratios typical for homogeneous freezing of aerosols. In
order to account for the ability of insoluble particles to act as ice
nuclei in heterogeneous ice formation processes, we assume a higher scavenging
ratio, namely 0.1, for
particles containing BC or dust, either externally or internally mixed
(insoluble and mixed modes). In the mixed-phase cloud regime
(T>238.15K), we assume an ice nucleation scavenging ratio of 0.1 for
all particle types. This rough estimate is based on the fact that, due to the
limited number of ice nuclei, only a fraction of cloud droplets freezes during
glaciation of liquid clouds, while the majority of the droplets
evaporate via the Bergeron–Findeisen process, thereby releasing large amounts of
aerosol mass originally scavenged
during liquid droplet formation. A ratio of 0.1 corresponds to typical
ratios of the concentrations of ice crystals and
cloud droplets in this regime (Korolev et al., 2003). Impaction scavenging does not depend on the
particle type.

Due to the extended mode structure of MADE3 with respect to the other two
aerosol submodels that can be used with SCAV in EMAC, i.e., the first version
of MADE and the Global Modal-aerosol eXtension (GMXe) submodel (Pringle et al., 2010), a number of modifications to SCAV were
required. The main conceptual difference is described in the following
subsection.

Figure 1Schematic representation
of the MADE3 submodel. The colors
represent the different chemical components. The dotted, solid, and dashed
lines correspond to the different mixing states (soluble, mixed, and
insoluble, respectively).

2.3 Aerosol processing in clouds and precipitation

Cloud and precipitation chemistry as well as wet deposition of both aerosol and
gas species are treated by the submodel SCAV in EMAC
(see Appendix B). We neglect ice-phase
chemistry here (including the uptake of gases onto ice particles) but include 35
chemical components and 45 reactions in the liquid-phase chemistry
scheme. Heterogeneous processes like the
formation of aqueous HNO3 from
gaseous N2O5 on droplet surfaces are also considered. We refer the
reader to the Supplement for more details on the chemical mechanisms adopted
in this work. For a description of the model representation of the different
aerosol scavenging processes, we refer to Appendix B1.

Resuspension of aerosol particles from evaporating/sublimating cloud particles
or precipitating hydrometeors is an important process to be represented, which has
recently been confirmed by Gao et al. (2016). For MADE3, we completely revised the
routines describing the redistribution of the resuspended aerosol. The basic assumptions for
the assignment of such residual aerosol to the MADE3 modes are described below.
Technical details and mathematics are provided in
Appendix B2. Note that, in the following, (i) “cloud
particles” refers to both ice crystals and liquid droplets suspended in clouds,
as well as to falling snowflakes and rain droplets; (ii) “cloud residual
aerosol” also includes “precipitation residual aerosol”; and (iii) “evaporation”
includes both evaporation of cloud and rain droplets, and
sublimation or melting plus subsequent evaporation of ice crystals and snowflakes. The following assumptions are made:

According to an operator splitting approach, we assume that activation of
aerosol particles into cloud particles occurs first, and impaction of
interstitial aerosol upon the cloud particles follows in an instantaneous
manner as a second step. We acknowledge that this constitutes a strong
simplification of the two interdependent processes, which may influence the
simulation of the cloud residual aerosol. When more measurement data on the
mixing state of cloud residual aerosol become available, the impact of this
simplification can be evaluated and the algorithm may then be
refined. However, as the influence of cloud particle coagulation on mixing
state is not represented in the model code, there will be some inevitable
error anyway.

In order to keep the complexity of the involved equations at a reasonable
level and to avoid underestimations of aerosol transformations within clouds,
we further assume that, during impaction scavenging, the interactions of
interstitial aerosol with cloud particles are as homogeneously distributed as
possible over the cloud particle population, regardless of the cloud
particles' aerosol cores (see Appendix B2 for more
details on this assumption).

Aerosol particles from the insoluble modes (dashed line in Fig. 1) cannot be activated into cloud
droplets in the present scheme, as they are assumed to be
hydrophobic. Nevertheless, they can serve as ice nuclei.

The chemical formation of water-soluble species within cloud droplets and
coagulation between cloud particles lead to accumulation of soluble aerosol
components inside cloud particles. To account for such effects, we assume
that all aerosol particles that were incorporated into cloud particles are
hydrophilic upon cloud particle evaporation. Hence, no residual aerosol is
assigned to the insoluble, hydrophobic modes.

Furthermore, we assume that – due to collection of other aerosol
particles, generation of aerosol mass inside cloud droplets, and coagulation
of cloud particles – aerosol particle cores of the cloud particles
resulting from activated Aitken-mode aerosol will
have grown from Aitken- to accumulation- or coarse-mode sizes when the cloud particles
evaporate. Hence, no residual aerosol is assigned to the Aitken modes.

2.4 Emissions setup

The emissions setup for the present study was in large parts designed by
Righi et al. (2013, see their Sect. 2) and includes wind-driven
sea spray emissions (Guelle et al., 2001), prescribed emissions of mineral
dust and volcanic sulfur (Dentener et al., 2006), terrestrial dimethyl sulfide
(DMS; Spiro et al., 1992), and natural SOA precursors (Guenther et al., 1995),
as well as prescribed anthropogenic and biomass burning
emissions representative of the year 2000 (Lamarque et al., 2010).
Emissions of the long-lived greenhouse gases (CO2 and CH4)
are implicitly considered by relaxing their near-surface mixing ratios to
observed values for the year 2000, based on data from the Advanced Global
Atmospheric Gases Experiment (AGAGE) and the National Oceanic and Atmospheric
Administration/Earth System Research Laboratory (NOAA/ESRL). This task is
fulfilled by the submodel TNUDGE. For the details on the treatment of aerosol
emissions in the model, we refer the reader to Righi et al. (2013). Here, we
only describe the parts of the emissions setup that were updated or required
MADE3-specific treatment.

Oceanic DMS
emissions are calculated according to a formulation by Liss and Merlivat (1986) in
ONEMIS, taking into account the dependence on wind speed and sea surface
temperature. In comparison to Righi et al. (2013), we use a more recent
climatological near-surface seawater DMS concentration dataset
(Lana et al., 2011) as an input to this parameterization.

As aerosol particle number concentrations, size distributions, and mixing states are not
included in most of the datasets that we use, we made typical assumptions for
mixing states and size distributions here. Following Cooke et al. (1999) and
Lohmann et al. (1999), we consider 80 % of the emitted BC and
50 % of the emitted POM to be hydrophobic and assign them to the
insoluble MADE3 modes. Note that in the present setup only combustion sources
of primary POM are considered and we assume that BC and POM are emitted as
internal mixtures in the form of soot particles. Consequently, the
hydrophilic fractions are assigned to the mixed modes. Depending on the
emission sector, SO4 is either assigned to soluble or mixed modes.
Where nucleation of ultrafine SO4 particles may play a role even in
aged emission plumes, we assign these particles to the soluble Aitken mode.
In the other cases, we assume that SO4 is efficiently scavenged by
BC/POM particles and, consequently, choose a mixed mode. Volcanic SO4
is assigned exclusively to the soluble modes, as we do not consider any
insoluble particles from volcanic emissions that could bear a coating.
Mineral dust emissions are assigned to the insoluble modes, in which
particles are assumed to be hydrophobic (Kaaden et al., 2009; Weinzierl et al., 2009),
whereas sea spray emissions are assigned exclusively to the soluble modes.
Unless explicitly specified in the datasets, i.e., for all emissions except
those of sea spray and mineral dust, we derive number emissions from the mass
emissions in analogy to the procedure employed by Righi et al. (2013). Under
the updated assumptions for the size distribution parameters given in
Table 2, the number emissions can thus be computed
from the species mass emissions. These number emission fluxes are added to
the corresponding MADE3 modes along with the mass emission fluxes from which
they were derived.

Table 2Size distributions assumed for emitted
particles. The term mmtot specifies the mass fraction
provided by the respective measured or prescribed mode; Dg and
σ specify, respectively, the median diameter and geometric standard
deviation of the lognormal distributions used to describe the number size
distributions of the modes. The “MADE3” columns show
the species and modes to which the emissions are assigned. The mode naming
convention follows Kaiser et al. (2014): one of the indices “k”, “a”,
or “c” is used to specify the Aitken, accumulation, or coarse mode,
respectively, and “s”, “m”, or “i” as a second index specifies the
soluble, mixed, or insoluble mode, respectively. The value “variable” in
the mass fraction and median diameter columns indicates that both number and
mass emissions are provided by the emission data source, so that number emissions do
not have to be
derived from the mass emissions. The emission sectors are
abbreviated as follows: “AIR” for aviation, “ANT” for anthropogenic
non-traffic, “AWB” for agricultural waste burning, “BB” for biomass
burning, “LAND” for land transport, and “SHIP” for shipping.

This section is organized as follows. First, we evaluate simulated
near-surface aerosol particle mass concentrations by comparing them to
measurements from four different station networks
(Sect. 3.1). We then move on to a comparison of the
vertical distribution of the simulated aerosol to aircraft measurements of BC
mass mixing ratio and of particle number concentration
(Sect. 3.2). In Sect. 3.3, we discuss
comparisons of simulated size distributions to measurements. Subsequently, we
present a method to compare global aerosol model output to size-resolved
electron microscopy particle composition measurements, together with a first
application (Sect. 3.4). As an aerosol measure derived
from the vertical distribution of particle concentrations, composition,
mixing state, and size distributions, we compare simulated AOD to satellite
measurements and station network data (Sect. 3.5). Finally, we
discuss global burdens of the
simulated aerosol particle species along with the species' tropospheric
residence times. Section 3.1–3.3
and 3.5 draw heavily on model evaluation with the help of the Earth
System Model eValuation Tool (ESMValTool;
Eyring et al., 2016).

When comparing global model output to observational data, several issues have to
be taken into account. As Schutgens et al. (2016a) pointed out, differences of
several tens of percent between simulations and measurements can arise simply due
to spatial sampling, when comparing grid-box average values to localized
observations. Furthermore, observed and simulated microphysical and chemical
aerosol properties may not always be fully consistent. Many measurement methods,
for instance, are only sensitive to a certain part of the atmospherically relevant
particle size spectrum. Specifically, to date, fine-mode particles (diameters up
to ∼1–2.5 µm) have received most attention in the
literature. This is especially important when comparing “total” aerosol
(species) mass and number concentrations. In addition, there is often not
a one-to-one correspondence between simulated and measured aerosol species. A
prominent issue in this context is related to measurements of “black
carbon”, “soot”, “elemental carbon”, “equivalent black carbon”, and
“refractory black carbon” which “synonymously refer to the most
refractory and light-absorbing component of carbonaceous combustion
particles” (Petzold et al., 2013). Different and partially inconsistent
terminology has been, and is, used in the corresponding literature
(Bond and Bergstrom, 2006; Petzold et al., 2013), which has to be kept in mind when comparing
simulated BC concentrations to measurement data. Finally, some measurements may
be inherently biased due to the method of particle sampling. According to
Ames and Malm (2001), for instance, fine-mode NO3 may be
underestimated in one station network (the Clean Air Status
and Trends NETwork, CASTNET), while it could be overestimated
in another (the Interagency Monitoring of PROtected Visual
Environments network, IMPROVE). The results of the comparisons
between MADE3 and the observations,
as well as the possible reasons for specific biases, are discussed in the following.

The discussion here is mostly descriptive and only gives some possible reasons
for deviations of simulations from observations. A thorough investigation of
such discrepancies would require a large number of sensitivity
simulations, including model experiments with different representations of processes
and/or different spatial resolutions. Although beyond the scope of
the present evaluation, this could be
conducted as part of future studies, and also serve for quantification
of simulation uncertainties.

3.1 Near-surface mass concentrations

Regular measurements within station networks provide both spatial and temporal
data coverage that is well suited for evaluation of global aerosol
models. Biases due to differences in timing of simulation output and
observations (Schutgens et al., 2016b) are likely small in this context, as the
measurements are typically taken by collecting particles on filters over several
days and subsequently analyzing these samples.

The 10-year average simulated near-surface mass concentrations are compared here to
the averages of available observational data in the period 1996–2005 from the
following station networks: IMPROVE (Hand et al., 2011) and CASTNET
(AMEC Environment & Infrastructure, 2015) in the US, the European
Monitoring and Evaluation Programme network (EMEP; Hjellbrekke, 2014), and the
Acid Deposition Monitoring Network in East Asia (EANET; Network Center for EANET, 2014). The
simulation data are always the sum of the contributions from all modes in the
lowermost model layer, i.e., up to ∼100 m. The comparison provides
an indication of the model's ability to reproduce the climatological state of
the Northern Hemisphere continental aerosol, where anthropogenic emissions are largest. As the
emission dataset is assumed to represent year 2000 conditions
(Sect. 2.4), the period for the observational data was chosen
symmetric to the year 2000. Note, however, that most of the stations (across
networks) do not provide complete temporal coverage of the years 1996–2005,
which may lead to biases. Specifically, all EANET data that went into the
comparison stem from the years after 2000. EMEP and EANET have fewer stations
and can thus provide less data than IMPROVE and CASTNET (see
Table 3).

Table 3Summary of the
model–observations statistical comparison of monthly mean near-surface
aerosol concentrations from the EMAC simulation with MADE3 and station
network data. 〈OBS〉
(〈MOD〉) stands for the arithmetic mean over all data
points of the observations (simulation), σobs
(σmod) for the standard deviations,
Fac2 for the percentage of
simulated values that are within a factor of 2 of the corresponding
observational values (i.e., 0.5OBS≤MOD≤2OBS),
and Npts for the number of data points, i.e., monthly averages,
that went into the comparison. See Appendix A2 in Righi et al. (2013) for more
details about the methodology.

Concentrations of the secondary inorganic aerosol species SO4,
NH4, and NO3 are the most widely measured and
typically have the longest records, while BC and POM are only measured in the
IMPROVE network. This subsection is ordered accordingly: the secondary species
are discussed first and the (mostly) primary aerosol components thereafter.

The geographical distribution of near-surface SO4 concentrations
(Fig. 2) is well reproduced over Europe
and east Asia, albeit with a small bias (Table 3). Over the US, agreement between
simulated and measured concentrations is better in the east than in the
northwest. The model mostly reproduces the spatial pattern in this region,
but it does not capture the west–east gradient seen in the observations,
and is biased high. The relative deviation of near-surface SO4 concentrations is
notably larger in the case of IMPROVE compared to the other networks
(Table 3). As Ames and Malm (2001) do not find systematic
differences in SO4 concentrations between co-located IMPROVE and CASTNET
measurements, a possible reason for this difference lies in the locations of the
IMPROVE stations. The relative deviations are largest in the northwestern part of the US
(Fig. 2), where most of the stations used
in the comparison are IMPROVE stations. These stations are mostly located in
national parks and wilderness areas, i.e., in rather clean environments, whereas
the large model grid boxes also cover more polluted areas in the vicinity of these areas.

Observed values of NH4 concentrations are spatially more heterogeneous than
those of SO4 concentrations, down to scales that cannot be captured by the
coarse resolution of the model. Furthermore, emissions of the NH4 precursor
NH3 are much more uncertain and variable than those of SO4 precursors.
That
said, model performance is mostly similar for NH4 and SO4
(Fig. 2). This also means that the
west–east gradient over the US is again underestimated. Note that, in the case of
NH4, most IMPROVE stations with available data are located in the eastern
part of the US, where agreement of the simulated
concentrations with the observations is slightly better than in the west (see
also Table 3). The north–south gradient over Europe
is generally well captured, with the exception of Spain and the western
Mediterranean.

The simulated near-surface NO3 concentrations agree remarkably well with
the observations across Europe
(Fig. 2). With respect to the IMPROVE data
for the US, it must be noted that several tens of percent of the simulated NO3
mass belong to the coarse modes. In contrast to CASTNET, however, IMPROVE
stations only sample particles up to a size of ∼2.6 µm(IMPROVE, 1995), so that deviations have to be expected, although
partly compensated by the tendency of IMPROVE to overestimate fine-mode NO3.
The comparison of NO3 concentrations to data from CASTNET and EANET yields
similar results as for NH4.

Only IMPROVE stations routinely monitor carbonaceous aerosol components. Hence,
the POM and BC simulation vs. observations comparison
(Fig. 3) includes only data from the
US. IMPROVE reports mass concentrations for organic carbon (OC), which were
converted to POM by multiplying by a factor of 1.4 (Dentener et al., 2006). Both the magnitude and the
spatial distribution of simulated near-surface concentrations generally agree
well with the observations. This was expected, since the primary aerosol
species POM and BC are only marginally affected by uncertainties associated
with gas- and liquid-phase precursor chemistry and gas–particle partitioning
which potentially cause distinct discrepancies in the case of secondary species.

To evaluate dust concentrations, we follow the same strategy as Aquila et al. (2011), who
compared simulated concentrations with the data based on a collection of
measurements from 22 stations around the world in the context of the
AEROsol Comparisons between Observations and Models (AEROCOM) project (Huneeus et al., 2011).
The results are shown in Fig. 4: the data from
the different stations are grouped in order of increasing dust load
according to the respective average dust concentrations (1 to 7: low, 8 to 16: medium,
and 17 to 22: high). EMAC (MADE3) generally underestimates dust concentrations,
especially
in comparison to the medium- and high-concentration stations, whereas the
annual cycle is captured reasonably well. As pointed out by
Aquila et al. (2011), this discrepancy could be due to the use of an offline
monthly mean climatology for dust emissions in the model, rather than
an online, wind-driven dust emission scheme. This could lead to
a misrepresentation of atmospheric dust transport and removal.
Furthermore, the climatology is representative of the
year 2000, which was characterized by relatively low dust emissions.
MADE3 simulations with more detailed dust emission parameterizations
are planned as a subject of future studies. It is interesting to note,
however, that the ability of MADE3 to reproduce dust concentrations has
improved considerably over the previous MADE-in version of Aquila et al. (2011), who used
the same input climatology for dust emissions.

The station networks also provide measurements of the sea spray components,
i.e., Na and Cl. However, their concentrations are
extremely low over the continents and, consequently, very sharp land–sea
gradients in the near-surface concentrations of these species occur. These
gradients cannot be accurately resolved by the model, which complicates the
comparison with the observations, especially for stations in coastal areas.
Hence, the station network data are not suited to evaluate sea spray aerosol.
For an evaluation of the simulated marine aerosol, we refer to
Sect. 3.5, where comparisons with satellite date are presented.

A comparison of simulated near-surface concentrations of various aerosol species
to observations at high latitudes is challenging, since observational data for
those regions are scarce and only a plausibility check could be performed here.
Simulated BC concentrations are close
to those measured over 2 years in North Greenland (as reported
by Massling et al., 2015), while simulated SO4 concentrations are roughly
a factor of 2 higher than the measured ones. Total aerosol mass concentrations
over Antarctica are larger compared to measurements taken by
Hara et al. (2014) during the austral summer 2007–2008.

Figure 4Climatological annual cycle of simulated (a) and measured (b) dust
surface-level concentrations in units of µg m−3. The observational
data were collected by Huneeus et al. (2011). The locations of the 22 stations considered
in the plot are shown in Fig. 6 of Aquila et al. (2011).

In conclusion, the simulated near-surface concentrations are mostly larger than
the corresponding observed values (Table 3). This
high bias is largest for the secondary aerosol components SO4, NH4, and
NO3 but only small in the case of BC and POM. It may indicate a too-low
efficiency of the deposition processes, which might also explain some of the
discrepancies discussed in the next subsection that deals with vertical profiles
of BC. That the overestimation is generally larger for soluble components
indicates that cloud processing may also play a role in the deviations. That
said, the statistics of our comparison with IMPROVE data are very similar to
those obtained with a previous EMAC version using MADE-in
(Aquila et al., 2011). The only exception here is NO3, which could not form on
coarse-mode particles in MADE-in and therefore could not reach as high
concentrations as in the present study. However, as mentioned above, IMPROVE
stations do not provide any insight into NO3 concentrations associated with
particles ≳2.6 µm.

The high bias of near-surface mass concentrations of secondary species found
here is not typically seen in studies using other global aerosol models.
Although (relative) discrepancies are often of similar magnitude to those
obtained here, the
deviations are typically more variable in their directions for different
species (e.g., Bauer et al., 2008; Mann et al., 2010; Pozzer et al., 2012; Lee et al., 2015). For instance,
EMAC (MADE3) simulates larger average sulfate concentrations than observed by all
considered station networks. The corresponding biases amount to 13 %, 38 %,
34 %, and 92 % compared to EANET, EMEP, CASTNET, and IMPROVE, respectively.
In contrast, Lee et al. (2015) found a similar high bias compared to IMPROVE
(95 %) but a low bias compared to observations from European sites (−13 %).
Other studies even show a general low bias. For example, the average sulfate
concentrations obtained by
Pozzer et al. (2012) show a low bias of −45 %, −16 %, and −28 % compared to
EANET, EMEP, and CASTNET, respectively. Hence, EMAC (MADE3) shows a tendency
towards enhanced sulfate concentrations. Nevertheless, the ability to
simulate several tens of percent of monthly mean values within a factor of 2
of the observations indicates a quality of EMAC (MADE3) that is similar to
that found in other model studies with this kind of analysis
(Pozzer et al., 2012; Kirkevåg et al., 2013). It should also be mentioned that, in contrast
to many other global aerosol models, EMAC (MADE3) performs quite well in the case
of black and organic carbon. However, we note that the primary goal
of the present study was not to improve on previous aerosol
climatologies but rather to show that our new model, with its additional
capabilities in terms of particle mixing state representation and coarse-mode
particle interactions, also produces reasonable climatologies and hence is
ready for investigating new topics that could not be addressed with the
former versions of the model.

3.2 Vertical distribution

It is even more delicate to evaluate the global 3-D aerosol distribution
than to evaluate the 2-D near-surface distribution. In contrast to the
multi-year time series of measurements provided by surface station
networks, aircraft measurements only sample aerosol along a specific
flight trajectory. Hence, both the spatial and temporal data coverage is limited. Although
arguably representative of the season and weather pattern during which
flights took place, there is much more uncertainty associated with the
comparison of climatological model output to aircraft measurements than with
that to station network data. Nevertheless, aircraft campaigns provide a uniquely valuable
way to measure vertical aerosol concentration profiles and are
routinely used to evaluate the performance of global aerosol models in
simulating the vertical aerosol distribution.

Here, we use observational data from campaigns between 1990 and 2014 over the
Pacific Ocean, over North and South America, over Europe, and within the Saharan dust outflow over the
Atlantic Ocean, as summarized in Table 4. Both BC
mass mixing ratios (aerosol mass per unit mass of air;
Fig. 5) as well as aerosol particle number
concentrations (Fig. 6) were used. Depending on
what a dataset provides, either mean values and standard deviations, or
medians and percentiles, or both are compared between simulation and
observations. Where data for individual flights are available, we show the
median of each flight in the comparisons. The variability of the measured
data includes spatial and temporal concentration variations during and
between the flights. The variability of the model output, however, reflects
the concentration variations around a climatological state, expressed by
long-term mean or median concentrations, respectively. Simulation output data
were selected from the grid boxes that include the flight trajectories and
from the output time steps corresponding to the days or months of the year
during which the flights took place (Table 4). This
means that model data are selected for these days/months for each year of the
simulation. Simulated meteorological-induced variability is captured well,
even if only data for single days are extracted from each simulated year. For
the comparisons, we vertically binned both the simulation and the measurement
data into 1 km intervals. In situ GPS altitude was converted to
ambient pressure using US standard atmosphere; this enabled the in situ to
model comparison.

Table 4Summary of relevant details and references for
the aircraft measurement datasets used in the evaluation of vertical
aerosol profiles simulated with MADE3 in EMAC. The values in parentheses in
the column “time” indicate the number
of measurement flights considered for the evaluation.

a Total of 9–10 flights per campaign; numbers not given
separately. b More than 700 profiles in total; numbers not given separately; number
of flights not given.

Figure 5BC mass mixing ratios (mmrs) in the
EMAC simulation with MADE3 (red) vs. measurements during various field
campaigns (black). Dashed lines and filled circles represent mean values;
dotted lines and whiskers represent standard deviations, which are only shown
in the direction of larger values for clarity. Solid lines stand for median
values. Light and dark shadings indicate the 10th to 90th, and 25th to 75th
percentiles, respectively. Hollow circles are the median values of individual
flights. Descriptions of the campaigns are provided in
Table 4 and in the text. Note that the vertical axis
of the left plot in each row applies to the other plots of that row as well,
and the horizontal axes of the plots in the lowermost row also apply to the
plots in the other rows.

The general picture that emerges from the comparison of the BC mass
mixing ratio profiles (Fig. 5), on the one
hand, is a comparatively good resemblance of simulated and observed near-surface
mass mixing ratios, particularly in polluted continental regions
close to major BC sources. For instance, in the case of the DC3 and CONCERT
campaigns, the simulated mixing ratios in the planetary boundary layer are
close to the observed values. This is consistent with the similarity of the
IMPROVE station measurements and the corresponding simulation results. On the
other hand, the simulated near-surface mixing ratios in remote areas
(e.g., the Pacific Ocean covered by the HIPPO campaigns), as well as those
simulated for higher altitudes, mostly exceed the
corresponding observations. A high bias of up to a factor of 10 occurs
in many cases. In this context, it should be mentioned that the data obtained
in missions initiated before 2003 used filter-based absorption measurements to
infer effective BC concentrations, whereas missions initiated after 2003 used
the Single Particle Soot Photometer (SP2) to report refractory BC concentration.
The BC measurements with SP2 cover a particle
size range of about 90–600 nm volume-equivalent diameter, assuming
1.8 g cm−3 void-free density, but for some datasets they have been
slightly corrected (generally by <15 %) to reflect the total accumulation-mode
BC mass. Except for fresh emissions very close to the sources,
which most of the data do not represent, we expect the SP2 to capture most
of the aerosol BC mass (Schwarz et al., 2006). Although underestimations of the
total BC mass in any non-accumulation size modes due to the detection
size limits in the measurements cannot be ruled out, discrepancies between model and observation of the order of
a factor of 10, as found here, are probably almost certainly
insensitive to this much smaller issue.

The high bias of the simulation with respect to the measured profiles could indicate
an underestimation of aerosol scavenging as also hypothesized in the previous
subsection. In addition, overestimated upward transport, possibly in convective
plumes, could also contribute. Ouwersloot et al. (2015) found increased
mixing ratios of an artificial tracer in the free troposphere when using
an improved convective transport scheme that was recently developed for future
versions of MESSy. This may mean that the general tendency of the simulated
aerosol mass mixing ratios to exceed the observed values could actually be even
larger, i.e., using a similar transport scheme here might lead to even larger
discrepancies. Previous studies with MADE
(MADE-in) rather showed a negative (slightly negative) bias of simulated
vs. measured concentrations (Lauer, 2004; Aquila, 2009), which could indicate that the
overestimation in the present work is caused outside the aerosol
microphysics submodel, possibly by the representations of
scavenging and vertical transport. However, the scavenging efficiency
also depends on the aerosol size distribution (see Sect. 3.3), which is largely
controlled by the aerosol microphysics submodel.

Figure 6Same as
Fig. 5 but for aerosol particle number
concentrations with various cutoff diameters.

Several other model studies included comparisons to the observational
datasets used here. For instance, Lohmann et al. (2007, ECHAM5-HAM) achieved
close agreement of the BC mass mixing ratio profiles for the Oct-AVE data
but, using the same model with some modifications to aerosol-related
mixed-phase cloud processes, Lohmann and Hoose (2009) found a similar
overestimation of the CR-AVE data as that in
Fig. 5. While Bauer et al. (2008, MATRIX) could
better reproduce the decline of the BC mixing ratios with altitude close to
the ground in the CR-AVE and TC4 data, EMAC with MADE3 performs better at
higher altitudes. In a recent study, Lund et al. (2017) demonstrated that
discrepancies between BC simulations with the OsloCTM2-M7 model and the HIPPO
data can be strongly reduced by modifications in the model representation of
BC wet scavenging. This again demonstrates that deficiencies in the model
descriptions of wet removal can play a key role in this context. BC
concentrations in the free troposphere are overestimated by many other models
(e.g., Koch et al., 2009, 2010; Schwarz et al., 2013; Allen and Landuyt, 2014; Schwarz et al., 2017). Several
authors, among them Kipling et al. (2013, HadGEM3-UKCA),
Wang et al. (2013, CAM5), and Allen and Landuyt (2014, CAM5), found a better agreement
with measured vertical profiles when improving the representation of
aerosol–convection interactions. This includes aerosol activation, vertical
transport, and wet removal in convective clouds. Note, however, that EMAC
with MADE3 performs better in simulating
upper tropospheric BC when compared to HIPPO data than the multi-model
average of the models that took part in phase II of the AEROCOM model
intercomparison project (Schwarz et al., 2013).

When comparing simulated aerosol particle number concentration profiles with
measurements (Fig. 6), we find a comparatively good
agreement over the Pacific Ocean, where both spatial and temporal
coverage by the observations is most extensive (more than 200 profiles
of the ultrafine condensation nuclei (UCN)-Pacific campaign; Clarke and Kapustin, 2002).
Note that the simulation values are the result of
an integration of the number size distribution from the cutoff
diameter (3 nm) upwards. Especially in the Northern Hemisphere, the agreement
is remarkable. In the equatorial latitudes, number concentrations agree
well in the lower troposphere, but simulated number concentrations are
smaller at high altitudes which could be a result of an underestimated
efficiency of new particle formation. Over the southern Pacific,
aerosol number concentrations are significantly underestimated in the
lower troposphere, which could be an indication that especially natural
sources of aerosol number are underrepresented in the model. For
instance, new particle formation mechanisms including natural organic
compounds (e.g., Kirkby et al., 2016; Tröstl et al., 2016) are
neglected. Since natural precursors might be very relevant for new particle
formation in the Southern Hemisphere where anthropogenic influences are
comparatively small, this model deficiency might lead to particularly
large discrepancies. The low bias of simulated aerosol number is not in
contradiction to the high bias of the BC concentrations discussed
above, since aerosol number is controlled by the large concentrations
of ultrafine particles, which provide only very small contributions to
aerosol mass and which are distinctively smaller than BC
particles. Nevertheless, this could be an indication of a misrepresentation
of the size distribution of such particles.

Similar to the comparison with the southern
hemispheric UCN-Pacific data, the simulated lower tropospheric aerosol
number concentrations are smaller than observed during INCA and
ACCESS-2, which again could be a consequence of missing aerosol
sources in the model. This deficiency can even affect the
concentration of larger aerosol particles in the cloud condensation
nuclei size range as reflected by the comparison with the INCA data. Hence,
future work should focus on improving the representation of natural background
aerosol, as also concluded by several other global aerosol modeling studies
(e.g., Carslaw et al., 2013, 2017).
In some cases, particularly in comparison to the INCA campaign, the model shows higher
ultrafine particle number concentrations in the upper troposphere.
A possible reason could be an overestimated nucleation
rate. Zhang et al. (2012, ECHAM-HAM2)
obtained a strong reduction in nucleation-mode number concentrations between
∼400 hPa and ∼150 hPa when switching from the
Vehkamäki et al. (2002, 2013) scheme employed in MADE3 to a more recent
parameterization.

Parts of the discrepancies discussed above could also result from temporal
inconsistencies between the simulations and the observational data. We apply
emission data for the year 2000, since a robust emission database is available for
that year (Lamarque et al., 2010). These emissions are assumed valid for the
years around 2000 (1996–2005). For consistency reasons, we adopt observational
data from this time period in most of the comparisons discussed in this article.
An exception is the data from recent
aircraft-based field campaigns, which were carried out up to 14 years after
2000. However, deviations between model and observations in the more
temporally dislocated cases are
similar to those found for campaigns close to 2000. A systematic trend in the
deviations does not occur. In addition, the deviations are clearly larger than
the changes in emission rates occurring between 2000 and the years of the
respective campaigns. Hence, internal model deficiencies, as described above,
are probably the main reason for the deviations, rather than trends in the
input data.

Figure 7Aerosol particle number size distributions in the EMAC simulation
with MADE3 (red) vs. ground-based measurements
(Putaud et al., 2003; Van Dingenen et al., 2004) during winter (W, a–c) and summer
(S, d–f) at
the same locations (columns). Each plot contains three measured size
distributions: one for the morning, one for the afternoon, and one for the
night hours. The three stations represent the following conditions (left to
right): rural, urban, and kerb side
(terminology adapted from Putaud et al., 2003). All
measurement locations fall into the same model grid box, so that the
simulated size distribution only differs between the top and bottom rows
but not between columns. Solid lines stand for median values; shadings
indicate the 25th to 75th percentiles. Note that the
vertical axis of the left plot in each row applies to the other plots
of that row as well, and the horizontal axes of the plots in the
lowermost row also apply to the other plots in the respective columns.

3.3 Size distributions

Size distributions provide more detailed information on the aerosol population
than integral particle number concentrations. Unfortunately, however, suitable
observational data for our evaluation are scarce, especially when it comes to
measurements above the ground. For the present study, we compared simulated size
distributions to data from ground-based measurements
(Putaud et al., 2003; Van Dingenen et al., 2004) and aircraft-based observations
(Petzold et al., 2002). The latter have the particular
advantage that size distributions were determined for different altitudes
throughout the troposphere. Simulated size distributions
are taken from the grid boxes corresponding to the geographical coordinates of
the measurements and only from those time steps (in each simulated year) that
correspond to the days or months of the observations. We found that variability
due to model meteorology is captured well with this approach.

The ground-based measurements were performed at 10 European stations that monitored aerosol
particle size distribution during at least one full season, i.e., either winter
(December, January, February) or summer (June, July, August) in the 1990s or
early 2000s. Putaud et al. (2003) fitted up to three lognormal modes to the
measured distributions for three times of the day, namely the morning, the
afternoon, and the night. Figure 7 shows a subset
of our comparisons, which serves to illustrate our results and the problems
associated with this type of evaluation.

Our main conclusion here is that the comparability of simulated and measured
size distributions can be strongly affected by the specific characteristics of
the local environments at the respective stations.
This is especially evident when comparing simulation output to
data from three stations that fall into the same model grid box, as we do in
Fig. 7. Only one size distribution can be realized
in this grid box at any given time in the model. While the model agrees
comparatively well with the measurements at the rural station (Fig. 7a, d),
it shows distinctively
smaller concentrations for all particle sizes when compared to measurements
from the urban background station (Fig. 7b, e) and the kerb-side station
(Fig. 7c, f). This was expected, since local concentration enhancements occurring
close to local sources cannot be resolved by the model with its large horizontal
resolution of about 300 km. The rural station (Harwell) might occasionally be
influenced by urban pollution since it is located in the vicinity of London.
Hence, the long-term median concentration at this station is expected to be closer
to the large-scale median concentration of the model grid box than the median values
from the other two stations, as the grid box contains both urban and rural
environments. This interpretation is consistent with comparisons to
measurements at natural background stations (other grid boxes; not shown) where the
model shows larger average concentrations than the observations which are
expected to be representative of the less polluted fractions of the respective
grid boxes.

Simulated near-surface size distributions over Europe appear to be strongly
affected by the emissions. Hence, as most
(prescribed) emissions in our simulation are considered as monthly averages, we see
little variability of the size distributions. Another deficit of the MADE3 aerosol particle size distribution is
the MADE3 output appearing almost unimodal in many cases, whereas the observations often show two
or more distinct modes. This finding is consistent with the result of the box
model test of MADE3 (Kaiser et al., 2014), and we now find it to be independent of
season and location. The discrepancy may be caused partially by differences
between the size distributions assumed to calculate particle number emission rates and the size distributions
obtained after assigning these emissions to the respective MADE3 modes (Sect. 2.4). In most cases, the widths
of the modes that were fit to the measured data are narrower than those assumed
in MADE3, where σ=1.7 and σ=2.0 for the Aitken and
accumulation modes, respectively. Simulations with alternative assumptions on
mode widths are intended to be the subject of future studies.

Further possible contributions to deviations between the simulation and the
observations could be related to the timing of simulation output and
measurements as well as to the new particle formation approach employed in MADE3. The lack of
temporal collocation of simulation output with measurement times may already
bias our results (Schutgens et al., 2016b). In addition,
Lee et al. (2013a, b) found that boundary layer nucleation of new particles
could contribute up to several tens of percent to the uncertainty in number
concentrations of particles larger than 50 nm. MADE3 includes
an empirical nucleation scheme (Vehkamäki et al., 2002, 2013). As several model
studies (e.g., Spracklen et al., 2006; Matsui et al., 2013; Makkonen et al., 2014; Pietikäinen et al., 2014)
suggest, the incorporation of more advanced nucleation schemes can lead to
a more accurate reproduction of observed aerosol particle number
concentrations.

Many of the arguments presented above also apply to the comparison of our
simulation data to data from the LACE campaign
(Fig. 8). The measurements were taken during July
and August 1998 at different altitudes over northeastern Germany
(Petzold et al., 2002). We use three-mode fits to the measured size distributions
for four to five individual flights here, depending on the flight
altitude. Again, the fitted modes are much narrower (σ≤1.6) than the
MADE3 modes.

Figure 8Aerosol particle size distributions in the reference simulation with
MADE3 (red) vs. measurements (black) for four to five
individual flights (depending on altitude) during the LACE campaign over
northeastern Germany. Dashed lines represent mean values; dotted
lines represent standard deviations, which are only shown in the direction
of larger values for clarity.

Notable differences between simulation and LACE data include the lack of the
coarse mode at lower altitudes and the accumulation mode peak in the upper
boundary layer/lower free troposphere from the simulation output. As the
measured coarse mode declines with altitude, it may have to do with local,
anthropogenically induced dust emissions that are not included in the
emission dataset used here. The peak at ∼ 300 nm in the upper boundary
layer/lower free troposphere measurements was caused by a forest fire aerosol
layer that cannot be reproduced in the simulation because this specific fire
is not contained in the emission dataset.

Looking at the remaining parts of the size spectrum and considering the model's
capabilities, we see good agreement of the simulated size distributions with the
LACE data. Furthermore, we find that agreement improves with altitude, i.e.,
with increasing particle age.

3.4 Size-resolved composition

To enable a specific evaluation of the new coarse-mode particle
representation in MADE3, it is useful to compare model output to size-resolved
particle composition measurements. However, such data rarely include coarse-mode
particles, and the correspondence between simulated and measured quantities is
not always straightforward. We therefore present a strategy for evaluating
simulated size-resolved aerosol composition with the help of electron microscopy
data of in situ sampled aerosol particles. For an initial application of
this strategy, we chose a dataset from measurements performed in January and
February 2008 at a ground station at Praia, Cabo Verde, by Kandler et al. (2011)
during the SAharan Mineral dUst experiMent 2 (SAMUM-2) field campaign (Ansmann et al., 2011).

Particle sizes as determined in the electron microscopy measurements are given
as equivalent circle diameters of the particles' projected areas. We assume that
these can be directly compared to the diameters derived from the simulated
aerosol particle number and component mass concentrations, the mode widths,
and the assumed component densities, since spherical particles are assumed in
the model (Kaiser et al., 2014).

The experimental analysis is performed on individual particles, i.e., 48 599
particles in the dataset used here. Overall, 13 major elements were detected in the
investigated particle population. Based on the relative contributions of the
elements to the particle volume, each particle is assigned to one of 12
different particle classes, e.g., sulfates, chlorides, oxides, and silicates
(see Kandler et al., 2011, for details).

For the comparison to model output, this procedure has a severe drawback. It
would require classification of the MADE3 particles according to the same, or
analogous, rules as the measured particles. However, since all particles within
each MADE3 aerosol mode are assumed to have the same composition (model assumption
of perfect internal mixture of all involved compounds), only particles of a maximum of
nine different compositions can coexist at the same time in each grid box of the
model. Classification of model particles, or rather modes, is therefore not
reliable from a statistical point of view. For instance, consider a mode that
contains both SO4 and Cl. With the model assumption of perfect internal
mixtures, its total volume can always be assigned to only one class, either to
the sulfates or to the chlorides. In reality, however, the mode would likely
contain both particles with a major contribution from sulfate
(assigned to the sulfate class by the measurements) and particles with a major
contribution from chloride (assigned to the chloride class by the measurements).
Hence, classification of the model modes would create unacceptable sampling biases.

Furthermore, nitrogen compounds only produce weak signals in the measurements,
and material from the sampling substrates can affect the analysis of
carbonaceous matter. Of the species simulated by MADE3, only SO4, Na,
Cl, and DU can therefore be determined reliably in the
measurements of contributions to particle composition.

For these reasons, we adopted a different view on the electron microscopy
data. In the approach employed here, the component masses of each analyzed
particle are assigned to one of five diameter “bins” according to the
particle's size. Only those components that can be compared to model output are
considered. The chloride
fraction is measured directly and considered to be derived exclusively from sea
spray. The sodium fraction is also measured directly, but for correspondence to
the MADE3 Na tracer (which represents the whole non-chloride sea spray mass in
the model), the sea spray sulfate fraction has to be added. The latter
can be derived from the measured chloride fraction under the assumption of a typical sea
spray composition, i.e., 54.6 % of chlorine atoms, and 2.82 %
of sulfur atoms, and under the assumption that all this sulfur is present in the
form of sulfate. The rest of the detected sulfur is also assumed to stem from
sulfate and can be compared to the MADE3 SO4 tracer. The mineral dust
contribution is derived from multiple elements that are typical of mineral dust
(silicon, aluminum, iron, magnesium, calcium, potassium, phosphorus, titanium,
and sodium).

Model output is binned into the same diameter intervals as the measurement data
by integrating the mass size distribution of each mode from the lower to the
upper bin boundary and then summing up the contributions of the individual
modes. Thus, measurement data and model output are brought to the same format
and can be compared.

An example comparison is shown in Fig. 9. The
measurement panel (Fig. 9b) displays the average particle composition over the
whole SAMUM-2 campaign (26 individual days or 48 205 particles). The rest of the
analyzed particles fell outside the size range presented here. With 3729
particles, the rightmost bin has the smallest database. For the model plot,
12-hourly output from the grid box that contains the measurement station was
averaged over the 10 evaluated simulation years, considering only the days of
the year when the measurements took place.

Figure 9Average size-resolved aerosol composition as simulated by the model
(a) and as measured during the SAMUM-2 campaign (b). Only
the mass fractions of species that can be compared between measurement data
and model output are depicted.

The result shown in Fig. 9 must be
interpreted with caution. It is not possible to exactly reproduce the
conditions during the SAMUM-2 campaign with the model setup used here, except by
chance. Especially, the monthly mean year 2000 DU and SO4 emissions in the
simulation may not be representative of the actual situation in the beginning of
the year 2008. Moreover, the meteorological features of the simulated years
(1996–2005), which largely impact the simulated mean aerosol properties at the
measurement site, might not correspond well to the specific meteorological
conditions in 2008. Local pollution sources cannot be resolved by the model
either. That said, the comparison reveals similarities between the simulated
and measured data in the decrease of the SO4 fraction and the
increases of the sea spray (Na plus Cl) and DU fractions with increasing
size. Major discrepancies, however, exist in the composition of the smallest
compared particles. We also analyzed the model biases in the individual
years, but the interannual variability (not shown) was found to be small;
hence, meteorology alone cannot explain the discrepancies. Model
misrepresentations, for instance, of the mineral dust particle size
distribution, the local sulfate concentration, or the competition between
nucleation and condensation of gaseous H2SO4 could also play a role.
On the other hand, the electron microscopy data analysis in particular
of the smallest size fraction might have a bias towards an underestimation of
sulfate particles due to their instability under the electron beam. Since the
number concentration of particles in this size fraction is comparatively
high, a thorough analysis, including comparisons of the measured and
simulated size distributions and also measurement uncertainties, should be
the subject of a separate study.

3.5 Aerosol optical depth

AOD provides an integral measure of the vertical aerosol column.
On the one hand, it can be computed from the simulated aerosol
properties discussed in the previous subsections, i.e., particle composition,
particle sizes, and their vertical distributions. On the other hand, AOD can
also be derived from measurements with ground-based and satellite-borne
radiometers. Here, in Fig. 10, we compare the simulated AOD to data
from the ground-based AErosol RObotic NETwork
(AERONET; Holben et al., 1998, 2001) and against satellite data from the
European Space Agency Climate Change Initiative (ESACCI) Swansea University (SU) Along-Track Scanning Radiometer 2 (ATSR-2) v4.21 aerosol product
(North et al., 1999; Bevan et al., 2012; Holzer-Popp et al., 2013; de Leeuw et al., 2015) and
from the MODerate resolution Imaging Spectroradiometer (MODIS) Level
3 Collection 6 data (Levy et al., 2013). Since annual mean
AOD regionally changed by up to 10 % during the last decade
(2000–2010, e.g., Yoon et al., 2014; Pozzer et al., 2015), only the year 2000 data are
used in the comparisons of annual mean AOD here. This does not apply to MODIS,
for which we considered the year 2003, i.e., the earliest year available in the
time period covered by the instrument.

Figure 10Annual mean AOD in the reference simulation with MADE3 (a,
background color) vs. observations from the AERONET network (a,
filled circles) and vs. satellite data from the ESACCI Swansea University
(SU) ATSR-2 v4.21 aerosol product (b, c) and from MODIS Level 3
Collection 6 (d, e). The comparison against satellite data is shown
as both absolute (b, d) and relative (c, e) difference.
“Pixels” in the panels correspond to the model grid. Results are shown for
the year 2000 (AERONET and ESACCI) and for 2003 (MODIS).

The results shown in Fig. 10 reveal that, in comparison to the measurements, the model simulates
up to ∼50 % higher AOD in the major
pollution and biomass burning plumes that originate in east Asia, central
Africa, and South America. This high bias is consistent
with the general tendency of the model to overestimate aerosol mass
concentrations as seen in Sect. 3.1
and 3.2. These high mass concentrations may also entail a higher
aerosol water content, which could increase AOD further. The sensitivity of AOD
to differences in hygroscopic growth, i.e., water uptake due to aerosol particle
hygroscopicity, was recently demonstrated, e.g., by Li et al. (2014). Furthermore,
the simulated AOD can be very sensitive to the scavenging scheme for aerosol
particles entrained into convective clouds. The choice of the scheme can lead
to several tens of percent different annual mean AOD values (Croft et al., 2012).
A better agreement is found in the case of less polluted areas such as the remote
oceans. The sign of deviations occurring in these areas shows spatial
variations. This could be an indication for misrepresentations of
either natural aerosol sources or long-range transport of anthropogenic
particles and should be the subject of further investigations in future studies. The model tends to underestimate AOD where
DU is abundant, especially over the Sahara and the Arabian
Peninsula. Potential reasons for this underestimation include the use of
prescribed monthly mean year 2000 DU emissions, the assumption on the DU size
distribution upon emission, and the DU representation in AEROPT, the submodel
that computes
aerosol optical properties (Sect. 2.1). Johnson et al. (2012) and
Nabat et al. (2012) found improved agreement of
simulated AOD with observations when using a parameterization with more of the
emitted DU mass in the coarse mode. Furthermore, the studies by
Zhao et al. (2013) and Mahowald et al. (2014) indicated that a modal representation
of DU particles with fixed mode widths may have unavoidable shortcomings.

AOD data from different satellite instruments do not agree perfectly but
show similar patterns in large parts of the globe. Consistent patterns of the two
satellite–model differences may therefore indicate areas where the model could be
biased. Remaining satellite–satellite differences do even occur for data
from the same instruments, if they are obtained with different
retrieval algorithms (e.g., Popp et al., 2016). Hence, one cannot expect perfect agreement of simulated
AOD with the observations either. It has, for example, been shown that MODIS
AOD is larger by about 0.03 on global average than ESACCI SU values, while the
ESACCI SU algorithm significantly overestimates AOD in dust regions such as the
Sahara (Lauer et al., 2017). Furthermore, uncertainties involved in the
model calculations of particle optical properties can of course also contribute
to deviations. While different models have different strengths and weaknesses,
it is interesting to note that many models have a low bias in AOD on a global
annual average basis
(e.g., Pozzer et al., 2012; Kirkevåg et al., 2013; van Noije et al., 2014; Lee et al., 2015; Michou et al., 2015) rather
than a tendency towards a high bias as seen here: 18 % (−5 %) with respect
to the ESACCI (MODIS) data. Relative underestimations in some of
the mentioned studies are actually larger than these values, so that we can
claim reasonable performance of EMAC with MADE3 as a global aerosol
model. Deviations of the order of 10 % should not be overinterpreted
anyway, as the lack of temporal collocation of the simulation output with the
measurement times can already lead to biases of this magnitude
(Schutgens et al., 2016b).

3.6 Tropospheric burdens and residence times

Although it is not an evaluation in the sense of a check against
observational data, a comparison of global tropospheric aerosol burdens and
residence times to estimates from other model studies is also instructive.
The burden mtot,a of aerosol species a is computed here as
the sum over the volume integrals of the mass concentrations ca in all
grid boxes. The simulated burdens are presented in Fig. 11 along with results from other modeling studies. The species' residence
times, tres,a, can be
derived from the burdens and the sums of the deposition fluxes,
Fdep,a, as

(1)tres,a=mtot,aFdep,a.

In the case of primary aerosol species, tres,a can alternatively be
estimated from the global emission fluxes Femis,a. Since
emissions are the only source of primary species, it can be assumed that
Fdep,a=Femis,a in an equilibrated global aerosol
budget. Due to the short lifetime of tropospheric aerosol, this equilibrium
assumption is well applicable here. Since Fdep,a can not yet be
quantified in our current model version, we use Femis,a instead
of Fdep,a in Eq. (1) to estimate
tres,a for the primary species DU, BC, and POM. The
quantification of tres,a for other aerosol constituents is
intended to be the subject of future studies.

The species' burdens simulated with MADE3 in
EMAC mostly fall within the ranges of previous estimates. For the secondary
inorganic species, i.e., SO4, NH4, and NO3, the
partitioning between the coarse and fine modes appears to play an important
role. While SO4 and NH4 are found predominantly in the fine
modes (>95 % on average), NO3 partitions roughly equally
between the fine and coarse modes on average. While the SO4 and
NH4 burdens simulated in the present study are at the upper end of
the range of available model results, the NO3 burden lies well within
the range spanned by the other studies. This could be an indication that
especially the lifetimes of fine-mode aerosol are comparatively long in our
model. The comparatively high NH4 burden in our simulation should be
interpreted with care, since corresponding values have been reported by only
three other studies.

The primary species' residence times estimated from our simulation amount to 8.84,
8.92, and 2.00 days for BC, POM, and DU, respectively. These values fall in
the ranges of [2.44, 9.60], [2.56, 9.52], and [1.56, 5.92] days, respectively,
spanned by the results of the previous studies mentioned in the caption of Fig. 11.

We implemented the aerosol microphysics submodel MADE3 into the global chemistry–climate
model EMAC as a successor to MADE and MADE-in. The new submodel version
includes nine aerosol species and represents three types of aerosol particles in three
different size ranges. With respect to its predecessors, MADE3 now explicitly
simulates the partitioning between the gas and the aerosol phase in the coarse
mode, as well as the interactions between the coarse and fine modes, and includes a fully
revised coupling to the scavenging submodel accounting for the wet deposition processes.

As a first application, we performed a 10-year model simulation.
To evaluate the model quality, we compared the simulation output to data from
a wide range of observations. These include aerosol (species) mass and number
concentrations, size distributions, and AOD from
surface-based, airborne, and satellite measurements. The results of these
comparisons are summarized below.

The main conclusion from the near-surface mass concentration comparisons is
that EMAC with MADE3 mostly captures the observed annual average spatial
patterns of all aerosol species included in the model. Best agreement was
obtained for black carbon and particulate organic matter, but they could only
be compared over the US. Among the other species, quantitative agreement is
typically best for SO4, with up to ∼70 % of the
simulated monthly mean values within a factor of 2 of the observations
(factors between 0.5 and 2). Concentrations of the nitrogen-containing
components, NH4 and NO3, are spatially less heterogeneous in
the simulations than in the observations. This is likely caused by the coarse
model resolution and by higher temporal variability of the precursor
emissions compared to those of SO4, which leads to larger
uncertainties in the emission datasets. We detected a high bias of the
average of the simulated values vs. the observations for nearly all species,
which might have to do with underestimated removal of fine-mode particles
from the atmosphere. Note, however, that near-surface mass concentrations
could only be evaluated over the Northern Hemisphere continents, with very
few exceptions. The comparisons demonstrate the ability of EMAC with MADE3 to
simulate several tens of percent of the monthly mean aerosol species
concentrations within a factor of 2 of the observations, which indicates
a quality of the model that is similar to that of other global aerosol
models.

The comparison of vertical BC mass mixing ratio and aerosol particle number
concentration profiles revealed that the model representations of aerosol
vertical transport and wet removal may need to be improved in order to avoid
overestimations of the upper tropospheric aerosol load. In addition, the
model description of new particle formation needs to be further developed
towards more robust representations of particle formation from inorganic and
also organic aerosol precursors. In this context, it should be stressed again
that discrepancies in the representation of vertical aerosol profiles are
a common feature of current global aerosol models and need to be the subject
of in-depth investigations and resulting model improvements in the future.

Simulated near-surface size distributions, or rather their level of agreement
with observations, were strongly affected by the coarse spatial and temporal
model resolution. The simulated distributions agreed well with measurements
in areas representative of continental background conditions. However,
a rather unimodal shape could often be seen in the simulation, whereas
observed distributions contained a separate nucleation mode in many cases,
for instance. This could be due to the coarse model resolution which impedes
the representation of local enhancements of ultrafine particles due to local
emissions. It could also be a consequence of the relatively wide MADE3 modes
in comparison to those fitted to the observational data. Furthermore, weaker
seasonal variability was found in the simulation than in observations across
Europe. For the future, we plan to deepen this analysis by extending the
simulated period and include comparisons to the data collected by
Asmi et al. (2011) and Birmili et al. (2016). This should also include
simulations with alternative assumptions for the mode widths and with
a higher spatial resolution.

The comparison of simulated AOD to ground-based and satellite observations
provided further evidence for some of the conclusions drawn above. Compared
to the observational data, our model shows larger AOD in regions affected by
anthropogenic pollution and biomass burning emissions. In contrast, the
simulated AOD is smaller compared to the
observations over regions where DU
dominates the aerosol composition. Together with deviations in sea-spray-dominated areas,
this shows the necessity of improving the representation of wind-driven dust and sea spray emissions in the model.

Our evaluation also included a comparison with
electron microscopy measurements, suggesting that the model is largely able
to simulate the dependence of aerosol composition on particle size. However,
these analyses need to be extended in the future to draw more robust
conclusions. Simulations of specific episodes during which measurements were
taken are required for this purpose, with the appropriate meteorology and
emissions. The size distribution of the measured aerosol particles should
also be taken into account for a thorough comparison, especially that of the
surface area available for SO4 condensation, in order to understand
the model–observation differences in the representation of fine-mode sulfate
in more detail. Such data, however, have to be measured with different
instruments. For some campaigns, both size-resolved composition and size
distribution measurements are available. MADE3 can be evaluated with these
data in the future by applying the method presented here.

We mentioned many sources of uncertainty in the parameters and
parameterizations that are part of the aerosol microphysics and transport
calculations, e.g., related to new particle formation and convective
transport or scavenging. In addition, there are numerous issues that have to
be taken into account when comparing simulations to observations. Among those
are the specific meteorological conditions and emissions, which influenced
the measured aerosol properties, the correspondence of measured and simulated
species, and the uncertainties inherent in the observations, which are rarely
reported. A detailed analysis of all these factors is beyond the scope of the
present study. Our main conclusion here is that, in all “disciplines”, the
simulation with MADE3 achieved a level of agreement with observations that
falls within the range of results reported by other authors from simulations
with their models. The same mostly holds for burdens and residence times of
the MADE3 aerosol components, so that the new submodel can be considered
ready for application.

Future studies with MADE3 should focus on the analysis and reduction of the
model discrepancies highlighted in the present evaluation. This could
include, for example, the consideration of observational uncertainties,
a detailed analysis of the scavenging efficiency and its dependency on the
aerosol size distributions and the underlying microphysical processes, as
well as simulations with higher spatial resolution and model experiments
focusing on the new particle formation processes
considering different nucleation parameterizations.

One of the intended applications of MADE3 in EMAC is the reassessment of the
aerosol-induced ship emissions effect on climate as described in the
introduction. We saw much higher aerosol nitrate concentrations over the
major shipping routes in our present simulation than in previous simulations
with MADE, where interactions of the coarse mode with the gas phase were
limited to the exchange of water in the condensed and gaseous phases. Hence,
previous conclusions, especially in terms of the assessment of low-sulfur
fuel scenarios, might have to be reconsidered.

Furthermore, MADE3 will be used as part of EMAC to assess climate effects of
the aerosol through modification of ice and mixed-phase cloud properties.
MADE3 is especially suitable for such applications due to its mixing state
representation with fully soluble, mixed, and insoluble particles in each of
the three size ranges of the Aitken, accumulation, and coarse modes.

MESSy is continuously further developed and applied by
a consortium of institutions. The usage of MESSy, including MADE3, and access
to the source code is licensed to all affiliates of institutions which are
members of the MESSy Consortium. Institutions can become members of the MESSy
Consortium by signing the MESSy Memorandum of Understanding. More information
can be found on the MESSy Consortium website
(http://www.messy-interface.org, last access: 9 January 2019). The
model configuration discussed in this paper has been developed based on
version 2.53 and will be part of the next EMAC release (version 2.54).

The submodel SCAV has undergone numerous updates since its original publication
(Tost et al., 2006a), e.g., concerning the ice phase as described by
Tost et al. (2010). Here, we briefly summarize the most important parts of the
algorithms as currently implemented for MADE3 aerosol, as not all of them have
been documented in the literature so far.

B1 Aerosol scavenging in and below clouds

Four operators are applied in the following sequence:

ice nucleation scavenging,

liquid nucleation scavenging,

snow impaction scavenging, and

rain impaction scavenging.

In SCAV, “nucleation scavenging” refers to both the actual nucleation of cloud
droplets or ice crystals, and the scavenging of aerosol by cloud particles due
to the aerosol particles' Brownian motion. The latter is currently not included
in many global aerosol models, although it may have a substantial impact on
particle number concentration (Pierce et al., 2015). The
term “impaction scavenging” summarizes the processes through which aerosol
particles are taken up by precipitation, i.e., falling hydrometeors. These processes include Brownian motion
of aerosol particles towards hydrometeors, impaction of hydrometeors upon
aerosol particles, and interception of aerosol particles by
hydrometeors. A scavenging rate, η, which is applied to the number and mass
concentrations of aerosol particles in grid boxes with clouds and/or
precipitation, is computed for each mode and each of the above
operators. It represents the aerosol fraction of the respective mode
incorporated into cloud or precipitation particles during a model time step Δt.

Cloud droplet nucleation is taken into account via an empirical function
(Tost et al., 2006a) that is applied to the hydrophilic particles, i.e., to
those in the soluble and mixed MADE3 modes:

(B2)ηlnunuc=2πΔtarctan2.5D̃g6,

with the dimensionless number median diameter of the respective aerosol mode D̃g (in units
µm, as for Dg). For Brownian motion, a semi-empirical formulation of the
scavenging coefficient by Pruppacher and Klett (1997) is used, which leads to
a scavenging rate of (Tost et al., 2006a)

(B3)ηlnuBr=1Δt1-exp-1.35LWCΔprcld2Δt.

Here, LWC is the cloud liquid water content (mass per unit volume),
and rcld is the effective cloud droplet radius, which is set
constant at rcld=17.5×10-6m. The aerosol particle
diffusivity Δp is computed as

where λair stands for the mean free path of air, is known as
the “slip correction”. Combining the two scavenging rates
(Eqs. B2–B3), one arrives at
the total liquid nucleation scavenged fraction per unit time:

(B6)ηlnu=ηlnunuc+ηlnuBr-ηlnunucηlnuBr.

The negative term accounts for the fact that aerosol particles cannot be
scavenged at the same time by both nucleation and Brownian motion.

Snow impaction scavenging is parameterized as

(B7)ηsim=1-exp-360m2skg-1FsΔt,

with the snow mass flux Fs per unit area and time. The rate
coefficient ηsim is applied to particles of all MADE3 modes.

where Fr is the rain mass flux per unit area and time. The terms
WiEi/rr,i are computed for six different values of
the rain droplet radius rr,i (i=1,…,6), namely for
0.1 mm, 0.2 mm, 0.5 mm, 1 mm, 2 mm,
and 5 mm. Ei is the collision efficiency of aerosol particles of size
Dg and rain droplets of size rr,i as parameterized by
Slinn (1984), with weights Wi based on the rain droplet radii
rr,i. Compared to measurements, Ei is likely underestimated
for fine-mode aerosol particles, which is a problem of any theoretically derived
formulation for this parameter (Wang et al., 2010). As for snow impaction
scavenging, the rain impaction scavenging rate is also applied to all MADE3
modes.

B2 Aerosol release from clouds and precipitation

In the case of evaporation or sublimation of cloud particles or precipitating
hydrometeors, aerosol residues are released. The following section describes
the algorithm for assigning the residual aerosol number and mass to the
respective MADE3 modes. The mode naming convention for this section is
described in the caption of
Table 2. Corresponding numbers are as follows:
ks=1, km=2, ki=3, as=4, am=5,
ai=6, cs=7, cm=8, and ci=9.

B2.1 Assignment of aerosol particle number concentrations

Let Nqlnu and Nqinu be
the number concentrations of aerosol particles from mode q that were activated
to form cloud droplets or ice crystals, respectively. Before all
other calculations, the insoluble ice nuclei number concentrations are assigned
to the corresponding mixed modes (according to assumption 4 in
Sect. 2.3) and the Aitken-mode cloud particle cores to the
corresponding accumulation modes (according to assumption 5):

These operations are not fully compatible with the nucleation scavenging
scheme of SCAV (Appendix B1) since the nucleation scavenging
rates considered by SCAV include not only nucleation of cloud droplets or ice
crystals but also Brownian motion scavenging within clouds. Separating the
rates of the different processes for considering the pure nucleation rate in
the assignment of cloud residues to the MADE3 modes would require fundamental
and very extensive changes of the SCAV core algorithm, which would be far
beyond the scope the present study. We therefore apply a simplified approach
here: Brownian motion scavenging of interstitial aerosol in non-precipitating
clouds is particularly important in the case of ultrafine aerosols
(e.g., Seinfeld and Pandis, 1998). Hence, large Brownian motion scavenging
efficiencies of non-precipitating hydrometeors (nucleation scavenging
operation in SCAV) can be expected in particular for the soluble Aitken mode,
due to the small particle sizes of nucleating aerosol particles included in
this mode. The insoluble and mixed Aitken modes as well as the accumulation
and coarse modes contain larger particles, which are rather subject to
nucleation scavenging than Brownian motion scavenging. Hence, we assume that
only Nkslnu and
Nksinu include major contributions of
Brownian scavenging. Therefore, this might lead to overestimations of the
number of aerosol particles served as droplet or ice nuclei. In order to
avoid this, we neglect Nkslnu and
Nksinu in Eq. (B9a) and
(B9b), respectively. Due to its very small particle size, the
soluble Aitken mode is only poorly activated to form cloud droplets and
possible underestimations of Naslnu due this
simplification are probably small. In the case of ice scavenging, 5 % of
Nks is assumed to serve as ice nuclei in the present version of
SCAV (Sect. 2.3 and Appendix B1). At high
concentrations of ultrafine soluble particles, this can lead to too-high
values of Nasinu compared to typical ice crystal
concentrations in pristine cirrus clouds. Neglecting
Nksinu therefore has an additional
benefit under these conditions. In the case of moderate number concentrations of
the soluble Aitken mode, errors in the activated number
Nasinu caused by neglecting
Nksinu are probably small compared to
the uncertainties inherent in the assumption of constant 5 % ice nucleation
scavenging, since variations of ice crystal number concentrations resulting
from varying homogeneous freezing conditions
(e.g., Kärcher and Lohmann, 2002) cannot be represented. Sensitivity
simulations showed, however, that neglecting
Nkslnu and
Nksinu in Eq. (B9a) and
(B9b) has only a marginal effects on the results. This means that
the effect of Brownian motion scavenging is small and further demonstrates
that possible errors due to the simplified representations of nucleation
scavenging are limited. Nevertheless, separate budgeting of processes in the
nucleation scavenging algorithm of SCAV could be the subject of future model
development work to enable a fully consistent representation of cloud
residual aerosol.

The number concentrations denoted by N will be the output values of the
mode assignment algorithm and are further modified as described in the
following. According to Eq. (B9a)–(B9h), cloud
particle cores now belong to one of the four modes: q=4, 5, 7, and 8 (corresponding
to “as”, “am”, “cs”, and “cm”). In the following, the symbol

(B10)Nqnuc=Nqlnu+Nqinu

is used to represent their cumulative number concentration in mode q. The
total number concentration of cloud particles is assumed to remain constant,
i.e.,

∑q={4,5,7,8}Nqnuc=constant

during the mode assignment process. In
the present setup, SCAV later reduces all Nqnuc to
10 % of their values to account for coagulation of cloud particles.

Now, the numbers of cloud particle cores per unit volume that are transferred to
different aerosol modes upon evaporation/sublimation of the cloud/precipitation particles are
calculated. Such transfers are due to impaction scavenging of other aerosol
particles. The symbol for the number of cores transferred from mode q to mode
r per unit volume shall be Nqr; the symbol for the total number of
interstitial mode q particles per unit volume collected by impaction
scavenging (Sect. B1) shall be
Nqimp.

Different cloud or precipitation particles can scavenge very different
numbers of interstitial aerosol particles from the various aerosol modes
leading to a large variety of modifications of the original cloud particle
cores. Representative mathematical descriptions of this complex system would
lead to overly complicated and error-prone formulas to describe mode
transfers of cores upon evaporation/sublimation of the cloud or precipitation
particles. Hence, the system is simplified here by

maximizing the transfer to mixed modes and

maximizing the transfer to coarse modes,

with a higher priority of the transfers to mixed modes. This deliberate
“overestimation” of the transfer rates is motivated by the fact that the
general reduction of Nqnuc (to 10 %; see above) does not
account for transfers of cloud particle cores to other modes, since it simply
reduces the total number of residues. The simplifications imply, for
instance, the assumption that the interactions of interstitial aerosol with
cloud particles are as homogeneously distributed as possible over the cloud
particle population, maximizing the number of transfers induced by impaction
scavenging.

It is acknowledged that these simplifications are somewhat arbitrary, but judging by previous simulation results –
e.g., a relatively small long-term mean effect of cloud processing on aerosol particle aging when compared to condensation of
trace gases – the associated error is expected to be tolerable. Nevertheless, different assumptions should be tested in the future
in order to explore the sensitivity of the simulation results to different representations of the mode transfers.

No transfer is required out of mode “cm”, as this mode represents the highest
degree of aerosol particle mixing and aging. For modes “cs” and “am”, the
calculation is straightforward, as cores from these modes can only be
transferred to mode “cm” (or remain in their respective mode). With the
simplifying assumptions described above, we obtain

is the fraction that mode q contributes to the total number concentration of
cloud particle cores. The MIN operation is required because cloud
or precipitation particles can collect multiple other particles via impaction scavenging, but
their cores can of course only be transferred to mode “cm” once.

The situation is more complicated for the “as” cores, as they can be
transferred to modes “am”, “cs”, and “cm”, depending on the aerosol
particles taken up by impaction scavenging. To simplify the system by maximizing
the transfer to the mixed and/or coarse modes, the following assumptions are made:

collected aerosol particles that contain insoluble material are
distributed as evenly as possible over the “as” cores;

fine, i.e., Aitken- and accumulation-mode, particles that contain insoluble
material are collected preferentially by cloud or precipitation particles that have not
collected coarse-mode particles which contain insoluble material; and

soluble coarse-mode particles are collected preferentially by cloud or precipitation
particles that have also collected fine-mode particles that contain
insoluble material.

Figure B1 may help the reader visualize these assumptions
and the associated transfers described in the following.

Figure B1Illustration of the transfer concept for mode “as” cloud particle
cores upon impaction scavenging of particles that induce such transfers. The
blue bar represents the number concentration of mode “as” aerosol
particles that have nucleated cloud particles; the boxes below it represent
the number concentrations of impaction-scavenged aerosol particles from the
three considered classes: coarse-mode particles that contain insoluble
material (black), fine-mode particles that contain insoluble material
(brown), and soluble coarse-mode particles (green). See text for explanation
of the symbols.

Following the outlined scheme, the direct transfer of cloud or precipitation particle cores from
mode “as” to mode “cm” by impaction of aerosol particles from modes “cm”
and “ci” is considered first. Let

(B14)Γ=MIN(gcm+gci,1)

be the fraction of mode r cores that has collected aerosol particles from the
coarse modes that contain insoluble material (which is actually independent of
r). Here,

(B15)gq=frNqimpNrnuc=Nqimp∑m=4,5,7,8Nmnuc

is the fraction of cores from mode r (or, in fact, from any mode) that has
collected aerosol particles from mode q. The number of cores directly
transferred from mode “as” to mode “cm” per unit volume can then be written
as

(B16)Nascm=ΓNasnuc.

Subsequently, if any “as” cores remain, transfers from mode “as” to mode
“cm” via collection of both fine-mode particles that contain insoluble
material and soluble coarse-mode particles are considered. Let

(B17)γ=MIN(gkm+gki+gam+gai,1)

be the analogue to Γ (Eq. B14) for collected fine-mode
particles that contain insoluble material. The expression for the transfer of
“as” cores to “cm” via collection of (at least) two aerosol particles
(one from “km”, “ki”, “am”, or “ai” and one from “cs”) per
cloud/precipitation particle then reads

(B18)Nas+2cm=MIN(γ,gcs,1-Γ)Nasnuc.

Once the terms for transfer to the mixed coarse mode have been established, and
in case any “as” cores remain, transfer from “as” to “am” or “cs”
without subsequent transfer to “cm” also has to be considered:

B2.2 Assignment of aerosol particle mass concentrations

As for the number concentrations, let mqlnu and
mqinu be the mass concentrations of aerosol
particles from mode q that were activated to form cloud droplets or ice
crystals, respectively. Furthermore, let mqch be the
aerosol mass per unit volume generated within, or lost from, the cloud particles
nucleated by mode q aerosol particles, which is due to cloud liquid-phase chemistry.

For consistency with the number treatment, the insoluble ice nuclei mass
concentrations are first assigned to the corresponding mixed modes and the
Aitken-mode cloud particle cores to the corresponding accumulation modes:

Note that, in contrast to the treatment of particle numbers
(Eq. B9a and B9b), mkslnu
and mksinu need to be considered here, since
Brownian motion scavenging contributes to the mass of cloud particle
residues while their number concentration does not change. As before,
the m will be the output values of our algorithm; the
𝔪 values are the input values, computed by SCAV using the fractions given
in Appendix B1. Note that there are no
mxich (x∈{k,a,c}) values because
ice-phase chemistry is not considered.

In order to simplify the following expressions, the cloud particle core mass
concentration mrnuc for each mode r=4,5,7,8 (“as”, “am”,
“cs”, and “cm”) is defined as the sum of the activated mode r aerosol
particle mass per unit volume, the aerosol mass generated per unit volume within
or on the cloud particles nucleated by mode r aerosol particles or lost from
these cloud particles, and the mass concentration of collected aerosol particles
that do not induce transfers of the residual from mode r (let
mqimp be the total mass concentration of mode q
particles that are collected by impaction scavenging; Appendix B1):

Now, the transferred mass concentrations are computed. For each mode (q=4, 5, 7, and
8), the mass concentrations of particles that induced the transfer and a fraction of
the core mass concentration mqnuc have to be transferred. As
mode “cm” is only a target mode for residuals, there is no transfer out of
this mode. All the mass that it receives by impaction scavenging stays in mode
“cm”. For the other three modes of cloud particle cores (“cs”, “am”,
“as”), the mass concentrations that have to be transferred are calculated
consistently with the number transfers:

This term denotes the fraction of collected fine-mode particles containing insoluble material
that is transferred with Nascm.

This term denotes the fraction of collected soluble coarse-mode particles that is transferred
with Nascm.

Similar notes apply to the first terms in
Eqs. (B27)–(B29). Note also that the terms with
denominator γNasnuc add up to 1, and the
terms with denominator gcsNasnuc do
so as well. This is due to the limits imposed on the number transfers (see
Sect. B2.1).

Finally, the mass concentration assignments are performed in an analogous manner
as is done for the numbers (again, read the arrows as “new value on the left-hand side
is computed from old values on the right-hand side”):

In the model, these expressions are applied to the individual species mass
concentrations since they are the central prognostic quantities. This is
legitimate since the individual species redistribute in proportion to the
total mass.

JCK, with the aid of JH and MR, implemented the aerosol
submodel MADE3 into the EMAC model system, performed the reference simulation
and its evaluation, and wrote the paper. PJ helped to implement MADE3 and to
prepare the simulation setup. HT assisted in the coupling of MADE3 to the
EMAC scavenging submodel SCAV. KK provided the electron microscopy data and
helped in the comparison with the model results. BW, DS, KH, JPS, and
AEP provided data from aircraft-based observations and assisted in the
corresponding model evaluation. TP provided the ESACCI data and contributed
to the comparison of the simulations with the satellite-based observations.
All coauthors assisted in the preparation of the
paper.

This study was supported by the DLR transport program (projects “Global
model studies on the effects of transport-induced aerosols on ice clouds and
climate” and “Transport and the Environment – VEU2”). Additional support
was received from the DLR space research program (project “Climate relevant
trace gases, aerosols and clouds – KliSAW”). The EMAC simulations were
performed at the German Climate Computing Center (DKRZ, Hamburg, Germany),
which also provided kind support for long-term storage of the model output
analyzed in this work. The DLR SP2 data were obtained with the support of the
Helmholtz Association under grant no. VH-NG-606
(Helmholtz-Hochschul-Nachwuchsforschergruppe AerCARE), the European Union
project ACCESS under grant agreement no. 265863, and DLR projects VolcATS and
CATS. The analysis of the DLR SP2 data was supported in part by the Center
for Advanced Studies at LMU, LMU Munich's Institutional Strategy LMUexcellent
within the framework of the German Excellence Initiative, and by the European
Research Council under the European Community's Horizon 2020 research and
innovation framework program/ERC grant agreement no. 640458 – A-LIFE. The
ESACCI data were provided by the ESA Climate Change Initiative and in
particular its Aerosol_cci project (http://www.esa-aerosol-cci.org,
last access: 9 January 2019) from the individual provider Swansea University
(Peter North). Mattia Righi received funding from the Initiative and
Networking Fund of the Helmholtz Association through the project “Advanced
Earth System Modelling Capacity (ESM)”. Konrad Kandler acknowledges support by the Deutsche
Forschungsgemeinschaft (DFG grants KA 2280/2 and /3). We are grateful to
Phoebe Graf (DLR) for processing the greenhouse gas data used in the TNUDGE
submodel, and to Mariano Mertens and Christof Beer
(DLR) as well as two anonymous
referees for their valuable comments on the manuscript. Last but not least,
we thank the whole MESSy team (developers and maintainers) for the invaluable
support.

The article processing charges for this open-access
publication were covered by a Research Centre of the
Helmholtz Association.

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Annexes, Unified Interpretations of the International Convention
for the Prevention of Pollution from Ships, 1973, as Modified by the
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Nordeng, T. E.: Extended versions of the convective parameterization scheme
at ECMWF and their impact on the mean and transient activity of the model
in the tropics, Technical Memorandum 206, European Centre for Medium-Range
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The implementation of the aerosol microphysics submodel MADE3 into the global atmospheric chemistry model EMAC is described and evaluated against an extensive pool of observational data, focusing on aerosol mass and number concentrations, size distributions, composition, and optical properties. EMAC (MADE3) is able to reproduce main aerosol properties reasonably well, in line with the performance of other global aerosol models.

The implementation of the aerosol microphysics submodel MADE3 into the global atmospheric...